A real-time locating system observes physician time-motion patterns during walk-rounds: a pilot study
Notice bibliographique
Résumé
BACKGROUND: Walk-rounds, a common component of medical education, usually consist of a combination of teaching outside the patient room as well as in the presence of the patient, known as bedside teaching. The proportion of time dedicated to bedside teaching has been declining despite research demonstrating its benefits. Increasing complexities of patient care and perceived impediments to workflow are cited as reasons for this declining use. Research using real-time locating systems (RTLS) has been purported to improve workflow through monitoring of patients and equipment. We used RTLS technology to observe and track patterns of movement of attending physicians during a mandatory once-weekly medical teaching team patient care rounding session endorsed as a walk-rounds format. METHODS: During a project to assess the efficacy of RTLS technology to track equipment and patients in a clinical setting, we conducted a small-scale pilot study to observe attending physician walk-round patterns during a mandatory once-weekly team rounding session. A consecutive sample of attending physicians on the unit was targeted, eight agreed to participate. Data collected using the RTLS were pictorially represented as linked points overlaying a floor plan of the unit to represent each physician's motion through time. Visual analysis of time-motion was independently performed by two researchers and disagreement resolved through consensus. Rounding events were described as a sequence of approximate proportions of time engaged within or outside patient rooms. RESULTS: The patient care rounds varied in duration from 60 to 425 minutes. Median duration of rounds within patient rooms was approximately 33% of total time (range approximately 20-50%). Three general time-motion rounding patterns were observed: a first pattern that predominantly involved rounding in ward hallways and little time in patient rooms; a second pattern that predominantly involved time in a ward conference room; and a third balanced pattern characterized by equal proportions of time in patient rooms and in ward hallways. CONCLUSIONS: Observation using RTLS technology identified distinct time-motion rounding patterns that hint at differing rounding styles across physicians. Future studies using this technology could examine how the division of time during walk-rounds impacts outcomes such as patient satisfaction, learner satisfaction, and physician workflow.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».